Artificial Intelligence in Cataract Surgery: A Systematic Review

Purpose

The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.

Methods

A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist.

Results

Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970.

Conclusions

The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning.

Translational Relevance

This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.

Patient-Centric Quality Standards

To ensure the quality, safety and efficacy of medicinal products, it is necessary to develop and execute appropriate manufacturing process and product control strategies. Traditionally, product control strategies have focused on testing known quality attributes with limits derived from levels administered in preclinical and clinical studies with an associated statistical analysis to account for variability. However, not all quality attributes have impact to the patient and those with the potential to impact safety and efficacy may not be significant when dosed at patient-centric levels. Therefore, achieving patient-centricity is understanding patient relevance, which is defined as the level of impact that a quality attribute could have on safety and efficacy within the potential exposure range. A patient-centric quality standard (PCQS) is therefore a set of patient relevant attributes and their associated acceptance ranges to which a drug product should conform within the expected patient exposure range. This manuscript describes historical perspectives details the way to create and leverage a PCQS in a variety of pharmaceutical product modalities.

Veteran influenza vaccination acceptance rates after completion of the COVID-19 vaccination series among historical influenza vaccine refusers

BACKGROUND

Vaccine hesitancy remains an obstacle in disease prevention. The recent COVID-19 pandemic highlighted this issue and may influence acceptance of other recommended immunizations. The objective of this study was to determine the association between receiving the COVID-19 vaccination and the subsequent acceptance of the influenza vaccination in a Veteran population that historically declined influenza vaccination.

METHODS

Influenza vaccination acceptance rates for the 2021-2022 influenza season were compared in patients who historically declined the influenza vaccine and either received or declined COVID-19 vaccinations. Logistic regression analysis was used to analyze factors associated with receiving influenza vaccination among vaccine hesitant individuals.

RESULTS

A higher proportion of patients who had received the COVID-19 vaccination(s) subsequently accepted the influenza vaccination compared to the control group (37% vs. 11%, OR = 5.03; CI 3.15-8.26; p = 0.0001).

CONCLUSION

Among previous influenza vaccine decliners, those who received COVID-19 vaccination had significantly higher odds of receiving subsequent influenza vaccination.

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights

Drusen are an important biomarker for age-related macular degeneration (AMD). Their accurate segmentation based on optical coherence tomography (OCT) is therefore relevant to the detection, staging, and treatment of disease. Since manual OCT segmentation is resource-consuming and has low reproducibility, automatic techniques are required. In this work, we introduce a novel deep learning based architecture that directly predicts the position of layers in OCT and guarantees their correct order, achieving state-of-the-art results for retinal layer segmentation. In particular, the average absolute distance between our model's prediction and the ground truth layer segmentation in an AMD dataset is 0.63, 0.85, and 0.44 pixel for Bruch's membrane (BM), retinal pigment epithelium (RPE) and ellipsoid zone (EZ), respectively. Based on layer positions, we further quantify drusen load with excellent accuracy, achieving 0.994 and 0.988 Pearson correlation between drusen volumes estimated by our method and two human readers, and increasing the Dice score to 0.71 ± 0.16 (from 0.60 ± 0.23) and 0.62 ± 0.23 (from 0.53 ± 0.25), respectively, compared to a previous state-of-the-art method. Given its reproducible, accurate, and scalable results, our method can be used for the large-scale analysis of OCT data.

Spatially regularized low-rank tensor approximation for accurate and fast tractography

Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain. Many tractography methods rely on models of multiple fiber compartments, but the local dMRI information is not always sufficient to reliably estimate the directions of secondary fibers. Therefore, we introduce two novel approaches that use spatial regularization to make multi-fiber tractography more stable. Both represent the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor, and recover multiple fiber orientations via low-rank approximation. Our first approach computes a joint approximation over suitably weighted local neighborhoods with an efficient alternating optimization. The second approach integrates the low-rank approximation into a current state-of-the-art tractography algorithm based on the unscented Kalman filter (UKF). These methods were applied in three different scenarios. First, we demonstrate that they improve tractography even in high-quality data from the Human Connectome Project, and that they maintain useful results with a small fraction of the measurements. Second, on the 2015 ISMRM tractography challenge, they increase overlap, while reducing overreach, compared to low-rank approximation without joint optimization or the traditional UKF, respectively. Finally, our methods permit a more comprehensive reconstruction of tracts surrounding a tumor in a clinical dataset. Overall, both approaches improve reconstruction quality. At the same time, our modified UKF significantly reduces the computational effort compared to its traditional counterpart, and to our joint approximation. However, when used with ROI-based seeding, joint approximation more fully recovers fiber spread.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Mass-Correlated High-Resolution Spectra and the Structure of Benzene

Mass-correlated rotational alignment spectroscopy resolved the rotational Raman spectra for 5 benzene isotopologues with unprecedented resolution. 13-C isotopologues were characterized at natural abundance. Fitted rotational constants allowed the analysis of effective and equilibrium bond lengths for benzene with sub-mÅ uncertainties. We found that previously reported experimental structures were wrong by multiple mÅ, due to unrecognized H/D isotope effects. Our results also refute recent experimental and theoretical literature claims of identical effective C-H and C-D bond lengths in benzene and reveal an isotope effect similar to that in other small molecules.

Structure of benzene from mass-correlated rotational Raman spectroscopy

We present high resolution rotational Raman spectra and derived geometry parameters for benzene. Rotational Raman spectra with sub-5 MHz resolution were obtained via high-resolution mass-correlated rotational alignment spectroscopy. Isotopologue spectra for C₆H₆, ¹³C–C₅H₆, C₆D₆, and ¹³C–C₅D₆ were distinguished through their correlated mass information. Spectra for ¹³C₆H₆ were obtained with lower resolution. Equilibrium and effective bond lengths were estimated from measured inertial moments, based on explicit assumptions and approximations. We discuss the origin of significant bias in previously published geometry parameters and the possibility to derive H,D isotope-specific bond lengths from purely experimental data.

We present high resolution rotational Raman spectra and derived geometry parameters for benzene isotopologues. Rotational Raman spectra with sub-5 MHz resolution were obtained via high-resolution mass-correlated rotational alignment spectroscopy.

Development of videoconferencing group educational program for patients with heart failure: A Delphi study

Funding Acknowledgements

Type of funding sources: None.

Background

During the COVID-19 outbreak, videoconference technology can help people avoid physical contact and minimise the exposure of healthcare providers to sick people as well as respiratory secretions. Videoconferencing can be beneficial for people with chronic conditions (such as heart failure) who have restricted mobility, live in remote and rural areas, or experience social loneliness. However, evidence for the effectiveness of using internet videoconferencing and its components accounted for the positive outcomes (such as knowledge, quality of life, self-care, healthcare utilization) is still lacking.

Purpose

To develop a feasible videoconferencing program for patients with heart failure that meets patients’ needs and preferences in Jordan.

Methods

The study involved two Delphi survey studies of three rounds each. The first Delphi survey involved 32 healthcare staff, with heart failure clinical experience, to obtain a consensus of opinion on a proposed group videoconferencing program for patients with heart failure. The second Delphi study involved seven staff of the information technology center, with experience in videoconferencing and using supporting applications, to obtain their consensus on the current capabilities, needs, barriers and facilitators of the healthcare system (providers) and patients (users) about information technology. For both Delphi studies, content analysis was used to analyze responses of participants in the first-Delphi round. Items resulting from the first-round analysis were summarized, listed, and rated (5-point Likert scale) in order to provide input and develop the second-round questionnaire. The percent agreement of participants was defined to be 80% or above to indicate their consensus.

Results

Healthcare providers prefer sessions to be led by the cardiologists and cardiac nurses, moving from simple to a complex topic, and the dose of information and frequency should be varied according to the needs and severity of heart failure. Videoconferencing strategy could improve patients’ satisfaction, knowledge and self-care, and adherence with their therapy plans. Jordan healthcare system has a capability to use videoconferencing strategy in Jordan.

Conclusion

This study addresses the equivocal evidence for the effectiveness of using internet videoconferencing between healthcare provider and patients. Establishing an effective videoconferencing program assists with meeting patients’ needs and preferences, accessing healthcare during COVID-19, reducing travel burden/costs as well as the risk of exposure to the virus, and ultimately improving clinical outcomes. This study will capture attention of healthcare providers to perform further studies targeting videoconferencing heart failure management programs

Contributions of Stereotactic EEG Electrodes in Grey and White Matter to Speech Activity Detection

Recent studies have shown it is possible to decode and synthesize speech directly using brain activity recorded from implanted electrodes. While this activity has been extensively examined using electrocorticographic (ECoG) recordings from cortical surface grey matter, stereotactic electroen-cephalography (sEEG) provides comparatively broader coverage and access to deeper brain structures including both grey and white matter. The present study examines the relative and joint contributions of grey and white matter electrodes for speech activity detection in a brain-computer interface.

[Experiences with digital care of patients with chronic and acute lung diseases during the SARS-CoV-2 pandemic]

BACKGROUND

Management of patients with respiratory disorders, such as asthma or chronic obstructive pulmonary disease (COPD), became challenging during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic due to infection prevention measures. To maintain care, a remote monitoring program was initiated, comprising a smartphone app and a Bluetooth spirometry device.

OBJECTIVE

To assess patient- and physician-related experience with remote monitoring.

MATERIAL AND METHODS

Structured questionnaires were developed to rate experiences from the patient or physician perspective on six-level Likert scales. Interactions between patients and physicians via the digital platform and overall utilization was analyzed.

RESULTS

A total of 745 patients with asthma, COPD, post-coronavirus disease 2019 (COVID-19) and other respiratory diseases were enrolled from 31 centers in Germany. Mean follow-up was 49.4 ± 12.6 weeks. Each participant submitted on average 289 measurements. Patient-reported experience with the remote monitoring program was positive, with the highest satisfaction reported for "Experience with home measurement" (1.4 ± 0.5; 99% positive), followed by "Communication/interaction" (1.8 ± 0.9; 83% positive) and "Overall satisfaction with program" (1.8 ± 0.8; 87% positive). In all, 70% reported subjective quality of life improvements related to participation in the program. Physician satisfaction with the program was also high with a mean rating of 2.2 ± 1.2.

DISCUSSION

App-based remote monitoring was successfully implemented in routine care during the SARS-CoV‑2 pandemic and demonstrated potential for improvements in care. Patient-relevant experience was positive in all dimensions and remote monitoring was well accepted. Physicians who participated in the program also expressed positive experiences, as demonstrated by a high level of interaction with the platform and positive evaluations of effects from the program.

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography

Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like smoking, hypercholesterolemia or hypertension. However, current diagnostic techniques often do not detect early stages of PAD. We theorize that PAD’s underlying cause atherosclerosis can be detected on color fundus photography (CFP) images with a convolutional neural network architecture, which might aid earlier PAD diagnosis and improve disease monitoring. In this explorative study a deep attention-based Multiple Instance Learning (MIL) architecture is used to capture retinal imaging biomarkers on CFP images of 135 examinations. To capture subtle variations in vascular structures, higher image resolution can be utilized by partitioning the CFP into patches. Our architecture converts each patch into a feature vector, and determines its relative importance via an automatically computed attention weight. Our best model achieves an ROC AUC score of 0.890. Visualizing these attention weights provides insights about the network’s decision and suggests ocular involvement in PAD. Statistical analysis confirms that the optic disc and the temporal arcades are weighted significantly higher (p < 0.001) than retinal background. Our results support the feasibility of detecting the presence of PAD with a modern deep learning approach.

[Smartphone-based fundus imaging: applications and adapters]

BACKGROUND

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published.

OBJECTIVE

The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far.

MATERIAL AND METHODS

A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions.

RESULTS

Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages.

CONCLUSION

SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

T2-Pseudonormalization and Microstructural Characterization in Advanced Stages of Late-infantile Metachromatic Leukodystrophy

PURPOSE

T2-weighted signal hyperintensities in white matter (WM) are a diagnostic finding in brain magnetic resonance imaging (MRI) of patients with metachromatic leukodystrophy (MLD). In our systematic investigation of the evolution of T2-hyperintensities in patients with the late-infantile form, we describe and characterize T2-pseudonormalization in the advanced stage of the natural disease course.

METHODS

The volume of T2-hyperintensities was quantified in 34 MRIs of 27 children with late-infantile MLD (median age 2.25 years, range 0.5-5.2 years). In three children with the most advanced clinical course (age >4 years) and for whom the T2-pseudonormalization was the most pronounced, WM microstructure was investigated using a multimodal MRI protocol, including diffusion-weighted imaging, MR spectroscopy (MRS), myelin water fraction (MWF), magnetization transfer ratio (MTR), T1-mapping and quantitative susceptibility mapping.

RESULTS

T2-hyperintensities in cerebral WM returned to normal in large areas of 3 patients in the advanced disease stage. Multimodal assessment of WM microstructure in areas with T2-pseudonormalization revealed highly decreased values for NAA, neurite density, isotropic water, mean and radial kurtosis, MWF and MTR, as well as increased radial diffusivity.

CONCLUSION

In late-infantile MLD patients, we found T2-pseudonormalization in WM tissue with highly abnormal microstructure characterizing the most advanced disease stage. Pathological hallmarks might be a loss of myelin, but also neuronal loss as well as increased tissue density due to gliosis and accumulated storage material. These results suggest that a multimodal MRI protocol using more specific microstructural parameters than T2-weighted sequences should be used when evaluating the effect of treatment trials in MLD.

CRASY: correlated rotational alignment spectroscopy of pyridine. The rotational Raman spectrum of pyridine and asymmetric fragmentation of pyridine dimer cations

We investigated the rotational Raman spectrum of pyridine monomers and pyridine dimers with mass-correlated rotational alignment spectroscopy (mass-CRASY) and ab initio calculations. The mass spectrum showed a strong signal for the protonated pyridine cation, which we assigned to asymmetric fragmentation of the dimer: ab initio calculations revealed facile proton transfer in the dimer cation and thermodynamically favorable asymmetric fragmentation. In the rotational spectrum correlated to the monomer mass channel, we assigned up to 40 lines for rotational states J ≤ 8. No spectrum could be assigned for the dimer, possibly due to the theoretically predicted presence of multiple dimer structures.

Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters

Background

Treatment with radiolabeled ligands to prostate-specific membrane antigen (PSMA) is gaining importance in the treatment of patients with advanced prostate carcinoma. Previous imaging with positron emission tomography/computed tomography (PET/CT) is mandatory. The aim of this study was to investigate the role of radiomics features in PSMA-PET/CT scans and clinical parameters to predict response to ¹⁷⁷Lu-PSMA treatment given just baseline PSMA scans using state-of-the-art machine learning (ML) methods.

Methods

A total of 2,070 pathological hotspots annotated in 83 prostate cancer patients undergoing PSMA therapy were analyzed. Two main tasks are performed: (I) analyzing correlation of averaged (per patient) values of radiomics features of individual hotspots and clinical parameters with difference in prostate specific antigen levels (ΔPSA) in pre- and post-therapy as a therapy response indicator. (II) ML-based classification of patients into responders and non-responders based on averaged features values and clinical parameters. To achieve this, machine learning (ML) algorithms and linear regression tests are applied. Grid search, cross validation (CV) and permutation test were performed to assure that the results were significant.

Results

Radiomics features (PET_Min, PET_Correlation, CT_Min, CT_Busyness and CT_Coarseness) and clinical parameters such as Alp1 and Gleason score showed best correlations with ΔPSA. For the treatment response prediction task, 80% area under the curve (AUC), 75% sensitivity (SE), and 75% specificity (SP) were obtained, applying ML support vector machine (SVM) classifier with radial basis function (RBF) kernel on a selection of radiomics features and clinical parameters with strong correlations with ΔPSA.

Conclusions

Machine learning based on ⁶⁸Ga-PSMA PET/CT radiomics features holds promise for the prediction of response to ¹⁷⁷Lu-PSMA treatment, given only base-line ⁶⁸Ga-PSMA scan. In addition, it was shown that, the best correlating set of radiomics features with ΔPSA are superior to clinical parameters for this therapy response prediction task using ML classifiers.

German regional variation of acute and high oral corticosteroid use for asthma

OBJECTIVE: 

To improve understanding of real-world asthma treatment and inform physician education, we evaluated regional variation in asthma prevalence and oral corticosteroid (OCS) use across Germany.

METHODS: 

We developed a machine learning gradient-boosted tree model with IMS® Disease Analyzer electronic medical records, which cover 3% of German patients. This model had a 91% accuracy in predicting the presence of asthma and chronic obstructive pulmonary disease. We applied the model to the IMS^® Longitudinal Prescription database, with 82% national coverage, to classify patients receiving treatment for airflow obstruction from October 2017-September 2018 in 63 regions in Germany.

RESULTS: 

Of 2.4 million individuals under statutory health insurance predicted to have asthma, 13.7%, 18.7%, 36.5%, 29.4%, and 1.7% received treatment classified as Global Initiative for Asthma (GINA) Steps 1, 2, 3, 4, and 5, respectively. Approximately 7-15% of those at GINA Steps 1-4 and 35% at Step 5 treatment received ≥1 acute OCS prescription (duration <10 days). Of patients receiving GINA Steps 1-4 and Step 5 treatments, 1-3% and 86%, respectively, received ≥1 high-dosage OCS prescription. Cumulative OCS dosage and percentages of patients receiving OCS differed substantially across regions, and regions with lower OCS use had greater use of biologic therapies.

CONCLUSIONS: 

Both acute and high OCS use varied regionally across Germany, with overall use suggesting patients are considerable risk of adverse effects and long-term health consequences.

Supplemental data for this article can be accessed at publisher's website.

Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography

Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36-52%) was significantly greater than the replicated one (overlap ratio 25-39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67-76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset.

De novo structure determination of butadiene by isotope-resolved rotational Raman spectroscopy

Isotope-selective rotational spectroscopy allows to calculate molecular structures independent of assumptions or theoretical predictions. Here, we present the first de novo structure determination based on mass-correlated rotational Raman spectroscopy, analyzing the carbon atom positions of butadiene. Mass correlation allowed us to analyze signals of rare 13C isotopologues at natural abundance, without interference from the main isotopologue signals. Fitted rotational constants and structural parameters confirm literature data from rovibrational spectroscopy of synthetic isotopologues and electron diffraction experiments.

Estimation of genomic breed composition of individual animals in composite beef cattle

Three statistical models (an admixture model, linear regression, and ridge-regression BLUP) and two strategies for selecting SNP panels (uniformly spaced vs. maximum Euclidean distance of SNP allele frequencies between ancestral breeds) were compared for estimating genomic-estimated breed composition (GBC) in Brangus and Santa Gertrudis cattle, respectively. Animals were genotyped with a GeneSeek Genomic Profiler bovine low-density version 4 SNP chip. The estimated GBC was consistent among the uniformly spaced SNP panels, and values were similar between the three models. However, estimated GBC varied considerably between the three methods when using fewer than 10 000 SNPs that maximized the Euclidean distance of allele frequencies between the ancestral breeds. The admixture model performed most consistently across various SNP panel sizes. For the other two models, stabilized estimates were obtained with an SNP panel size of 20 000 SNPs or more. Based on the uniformly spaced 20K SNP panel, the estimated GBC was 69.8-70.5% Angus and 29.5-30.2% Brahman for Brangus, and 63.9-65.3% Shorthorn and 34.7-36.1% Brahman in Santa Gertrudis. The estimated GBC of ancestries for Santa Gertrudis roughly agreed with the pedigree-expected values. However, the estimated GBC in Brangus showed a considerably larger Angus composition than the pedigree-expected value (62.5%). The elevated Angus composition in the Brangus could be due to the mixture of some 1/2 Ultrablack animals (Brangus × Angus). Another reason could be the consequences of selection in Brangus cattle for phenotypes where the Angus breed has advantages.

Association of Visual Function Measures with Drusen Volume in Early Stages of Age-Related Macular Degeneration

Purpose

To assess which visual function measures are most strongly associated with overall retinal drusen volume in age-related macular degeneration (AMD).

Methods

A total of 100 eyes (16 eyes with early AMD, 62 eyes with intermediate AMD, and 22 eyes from healthy controls) were recruited in this cross-sectional study. All subjects underwent several functional assessments: best-corrected visual acuity (BCVA), low-luminance visual acuity (LLVA), visual acuity (VA) measured with the Moorfields Acuity Chart (MAC-VA), contrast sensitivity with the Pelli-Robson test, reading speed using the International Reading Speed texts, and mesopic and dark-adapted microperimetry. Drusen volume was automatically determined based on optical coherence tomography using an approach based on convolutional neural networks. The relationship between drusen volume and visual function was assessed with linear regressions controlling for confounders.

Results

Mean drusen volume and MAC-VA differed significantly among all AMD stages and controls (P < 0.001). In univariate linear regression, LLVA, MAC-VA, contrast sensitivity, and mesopic and dark-adapted microperimetry were significantly negatively associated with the overall drusen volume (all P < 0.006). After controlling for AMD stage, age, and the presence of subretinal drusenoid deposits, MAC-VA and mesopic and dark-adapted microperimetry were still significantly associated with drusen volume (P = 0.008, P = 0.023, and P = 0.022, respectively).

Conclusions

Our results suggest that MAC-VA, as well as mesopic and dark-adapted microperimetry, might indicate structural changes related to drusen volume in early stages of AMD.

A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications

Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.

SHORE-based detection and imputation of dropout in diffusion MRI

PURPOSE

In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values.

METHODS

Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator-based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers.

RESULTS

Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives.

CONCLUSIONS

Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi-shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).

4D-CT-based motion correction of PET images using 3D iterative deconvolution

Objectives

Positron emission tomography acquisition takes several minutes representing an image averaged over multiple breathing cycles. Therefore, in areas influenced by respiratory movement, PET-positive lesions occur larger, but less intensive than they actually are, resulting in false quantitative assessment. We developed a motion-correction algorithm based on 4D-CT without the need to adapt PET-acquisition.

Methods

The algorithm is based on a full 3D iterative Richardson-Lucy-Deconvolution using a point-spread-function constructed using the motion information obtained from the 4D-CT. In a motion phantom study (3 different hot spheres in background activity), optimal parameters for the algorithm in terms of number of iterations and start image were estimated. Finally, the correction method was applied to 3 patient data sets. In phantom and patient data sets lesions were delineated and compared between motion corrected and uncorrected images for activity uptake and volume.

Results

Phantom studies showed best results for motion correction after 6 deconvolution steps or higher. In phantom studies, lesion volume improved up to 23% for the largest, 43% for the medium and 49% for the smallest sphere due to the correction algorithm. In patient data the correction resulted in a significant reduction of the tumor volume up to 33.3 % and an increase of the maximum and mean uptake of the lesion up to 62.1 and 19.8 % respectively.

Conclusion

In conclusion, the proposed motion correction method showed good results in phantom data and a promising reduction of detected lesion volume and a consequently increasing activity uptake in three patients with lung lesions.

Diffusion MRI visualization

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging

Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.

High-resolution rotational Raman spectroscopy of benzene

We present a rotational Raman spectrum for benzene with single-MHz resolution, more than a 100-fold improvement on literature data and sufficient to partially resolve K-splitting in some bands. Spectra for a frequency range of 0 to 500 GHz were measured through the observation of a coherent rotational wave packet in the time domain over a time scale of 1 microsecond. Spectroscopic frequencies were referenced to a GPS-stabilized clock. Fitted molecular constants of B = 5689.2671(±52) MHz, DJ = 1178(±50) Hz, and DJK = -2300(±120) Hz agree with results from some high-resolution rovibrational and rovibronic spectra but contradict others.

Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

DT-MRI Streamsurfaces Revisited

DT-MRI streamsurfaces, defined as surfaces that are everywhere tangential to the major and medium eigenvector fields, have been proposed as a tool for visualizing regions of predominantly planar behavior in diffusion tensor MRI. Even though it has long been known that their construction assumes that the involved eigenvector fields satisfy an integrability condition, it has never been tested systematically whether this condition is met in real-world data. We introduce a suitable and efficiently computable test to the visualization literature, demonstrate that it can be used to distinguish integrable from nonintegrable configurations in simulations, and apply it to whole-brain datasets of 15 healthy subjects. We conclude that streamsurface integrability is approximately satisfied in a substantial part of the brain, but not everywhere, including some regions of planarity. As a consequence, algorithms for streamsurface extraction should explicitly test local integrability. Finally, we propose a novel patch-based approch to streamsurface visualization that reduces visual artifacts, and is shown to more fully sample the extent of streamsurfaces.

Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review

PURPOSE

To assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD).

METHODS

Following a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias.

RESULTS

Thirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92.

CONCLUSIONS

Automated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially.

TRANSLATIONAL RELEVANCE

The development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.

[Epi-Bowman Keratectomy: Clinical Evaluation of a New Method of Surface Ablation]

PURPOSE

A new device for epithelial abrasion before excimer laser surface ablation or corneal cross-linking (CXL) has recently been introduced (Epi-Clear^™, Orca Surgical, Kiryat-Shmona, Israel). We have reviewed the literature on the clinical results, potential benefits and drawbacks of this instrument, compared to other methods of epithelial removal.

METHOD

Literature search for "Epi-Bowman Keratectomy", "Epi-clear", and "Epikeratome" yielded 1 peer-review publication, 1 non-peer-review publication, 18 posters and presentations at international conferences (European Society of Cataract and Refractive Surgeons [ESCRS] and American Society of Cataract and Refractive Surgery [ASCRS]) on the use of the Epi-Clear™ device before surface ablation, 2 posters on the use of Epi-Clear before corneal crosslinking and 1 presentation on the experimental use of Epi-Clear for removal of a pterygium.

RESULTS

Comparison of laser ablation after epithelial removal with the Epi-Clear device (Epi-Bowman Keratectomy™, EBK™) to other established methods of surface ablation, i.e. alcohol-assisted PRK or PRK with a metallic scraper, EBK, suggests that the results are generally similar. Pain perception, haze formation, and epithelial healing are reported to be better than with conventional surface ablation methods. Studies evaluating the use of the Epi-Clear device before CXL report that the healing time is significantly reduced and that less pain is perceived.

CONCLUSION

The Epi-Clear device seems to be a promising new option for epithelial removal before refractive laser ablation, although a convincing explanation for its potential superiority is still missing. In contrast, when the Epi-Clear device is used before CXL, then the Bowman's layer remains intact; this may provide an adequate explanation for the reported benefits of this application. However, currently available studies are of low level of evidence, so that more prospective randomised trials are needed for a robust evaluation of this treatment.

[New Developments in Cataract Surgery]

Recent technological innovations in cataract surgery have made the procedure even more precise and safe and the odds of having a highly satisfied patient even higher. These innovations include visualisation systems - such as intraoperative aberrometry - which are particularly helpful when it comes to implanting toric IOLs, where even a slight rotation or misalignment can significantly reduce the postoperative visual quality. Another way to ensure the exact positioning of a toric IOL is to create a mark by making an intrastromal incision using the femtosecond laser. The latter technology has increased the precision of capsulotomy and other steps of the operation and has been successfully employed in patients with a challenging clinical profile, including paediatric and hypermature cataracts. The femtosecond laser, however, induces an increase in intraocular prostaglandins, which can lead to miosis. Applying topical NSAIDs before starting surgery has proved to be effective in coping with the consequences of the increase in prostaglandins. Good vision without using glasses for near, intermediate and far distances remains a goal for many patients. IOLs with extended depths of focus (EDOF) technology can provide this comfort - to some but not all patients. An intraocular sensor, Eyemate, that is implanted during cataract surgery, enables the glaucoma patient to check his or her IOP at any time and improves the management of glaucoma and its main risk factor, elevated IOP. Several methods - drugs or nutritive agents - are said to prevent cataractogenesis. These studies have probably to be taken with the proverbial grain of salt.

Post-arc Current Measurement in Mechanical Circuit Breakers for Hvdc Applications

Post-arc currents contain valuable information on the breaker’s performance. In this paper, the dimensioning procedure for a measurement system for such currents, based on a diode clamped shunt resistor is presented. Based on synthetic low and high power tests, the performance of a mock-up is evaluated. Finally, the post-arc current of a model gas circuit breaker, designed for use in DC applications, is measured.

Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI

OBJECTIVE

We investigate how known differences in myelin architecture between regions along the cortico-spinal tract and frontal white matter (WM) in 19 healthy adolescents are reflected in several quantitative MRI parameters that have been proposed to non-invasively probe WM microstructure. In a clinically feasible scan time, both conventional imaging sequences as well as microstructural MRI parameters were assessed in order to quantitatively characterise WM regions that are known to differ in the thickness of their myelin sheaths, and in the presence of crossing or parallel fibre organisation.

RESULTS

We found that diffusion imaging, MR spectroscopy (MRS), myelin water fraction (MWF), Magnetization Transfer Imaging, and Quantitative Susceptibility Mapping were myelin-sensitive in different ways, giving complementary information for characterising WM microstructure with different underlying fibre architecture. From the diffusion parameters, neurite density (NODDI) was found to be more sensitive than fractional anisotropy (FA), underlining the limitation of FA in WM crossing fibre regions. In terms of sensitivity to different myelin content, we found that MWF, the mean diffusivity and chemical-shift imaging based MRS yielded the best discrimination between areas.

CONCLUSION

Multimodal assessment of WM microstructure was possible within clinically feasible scan times using a broad combination of quantitative microstructural MRI sequences. By assessing new microstructural WM parameters we were able to provide normative data and discuss their interpretation in regions with different myelin architecture, as well as their possible application as biomarker for WM disorders.

[Technique-based game for daycare visitors with and without dementia : Effects, heuristics and correlates]

BACKGROUND

Playing of old people with or without dementia have not yet been substantially investigated.

OBJECTIVE

This study deals with the acceptance and impact of a tablet-based memory game, which was played on a weekly or semiweekly basis by visitors in two daycare units.

MATERIAL AND METHODS

Within the framework of focus groups the technical system was adapted for elderly users. The video-assisted data at the level of the game and the dynamics were investigated with respect to interaction and communication.

RESULTS

The analysis of psychological observation forms and game protocols, which were conducted over a period of 3 months, indicated different effects of the game on psychosocial and cognitive activation. The individual memory cards in particular served as an intensification of communication and a stimulation of episodic memory. Finally, with video analysis during the whole game setting three theoretical relationship patterns of the spheres playing and speech could be depicted.

CONCLUSION

Coherence, separation and incoherence of playing and speech are different forms of interaction in which individual and collaborative competences of people with and without dementia can be visualized. Furthermore, the study provides evidence for the cultural theory of playing by Huizinga.